The impact of rate heterogeneity on inference of phylogenetic models of trait evolution

Abstract

Rates of trait evolution are known to vary across phylogenies; however, standard evolutionary models assume a homogeneous process of trait change. These simple methods are widely applied in small‐scale phylogenetic studies, whereas models of rate heterogeneity are not, so the prevalence and patterns of potential rate variation in groups up to hundreds of species remain unclear. The extent to which trait evolution is modelled accurately on a given phylogeny is also largely unknown because studies typically lack absolute model fit tests. We investigated these issues by applying both rate‐static and variable‐rates methods on (i) body mass data for 88 avian clades of 10–318 species, and (ii) data simulated under a range of rate‐heterogeneity scenarios. Our results show that rate heterogeneity is present across small‐scaled avian clades, and consequently applying only standard single‐process models prompts inaccurate inferences about the generating evolutionary process. Specifically, these approaches underestimate rate variation, and systematically mislabel temporal trends in trait evolution. Conversely, variable‐rates approaches have superior relative fit (they are the best model) and absolute fit (they describe the data well). We show that rate changes such as single internal branch variations, rate decreases and early bursts are hard to detect, even by variable‐rates models. We also use recently developed absolute adequacy tests to highlight misleading conclusions based on relative fit alone (e.g. a consistent preference for constrained evolution when isolated terminal branch rate increases are present). This work highlights the potential for robust inferences about trait evolution when fitting flexible models in conjunction with tests for absolute model fit.

Model Adequacy and Microevolutionary Explanations for Stasis in the Fossil Record

“…Comparing how alternative models fit a particular data set allows for a relative evaluation of how each model explains the data, but this approach does not guarantee that the best model out of the alternatives describes the data particularly well (e.g., Pennell et al 2015). While tests of absolute fit of models within phylogenetic comparative methods continue to be developed and investigated (e.g., Slater and Pennell 2013;Pennell et al 2015;Chira and Thomas 2016), this has not been a focus for studies fitting models to fossil sequence data. We have argued that development of test statistics to evaluate model adequacy for studies of the fossil record is important if the goal is to reliably estimate model parameters of interest.…”

Macroevolutionary bursts and constraints generate a rainbow in a clade of tropical birds

“…In our dataset, simulated values of one statistic (C var ) frequently deviated from empirical values because of unaccounted-for rate variation in our best-fit, constant rate model. Even at relatively shallow phylogenetic scales, body size and plumage color exhibit rate heterogeneity [23,46,47]. Accounting for rate shifts by testing the Delta model was critical for accurately characterizing the evolution of highly variable regions, which may be rapidly shifting between several discrete states or diversifying due to sexual selection.…”

Adaptive radiation and the evolution of nectarivory in a large songbird clade

“…To compare model fit between alternative evolutionary methods, we used the approach outlined in Cooney et al (2017) that builds on the methodological developments of Pennell et al (2015) and Chira and Thomas (2016), to calculate log-likelihoods describing the relative fit of different models of continuous trait evolution to each pPC axis. These analyses were performed using the fitContinuous function in the R package geiger (Harmon et al 2008).…”